Systems and methods for generating adapted content depictions

ABSTRACT

A method for generating a content depiction of particular content that includes a machine learning system programmed to receive profile data representing preferences for content. The machine learning system identifies preferences for content features based upon the profile data, accesses content data representing the particular content and other content, and classifies features of the content data and content structure data within a content structure database system according to content categories. The machine learning system generates a content structure depiction of the particular content by combining content structure data from the content structure database system, wherein the combining is based upon correlating the identified preferences of the profile with the classified content categories. The machine learning system receives feedback data responsive to the content depiction and reprograms a configuration of the machine learning system for generating a content depiction based upon the feedback data.

Cross-Reference to Related Applications

This application claims priority to U.S. Provisional Application No.62/979,785 filed February 21, 2020, the content of which is herebyincorporated by reference herein in its entirety.

Background

The present disclosure relates to systems and processes for generatingimage depictions of content based upon profiled preferences.

Summary

Depictions (e.g., posters, images) of content (e.g., movies) arecommonly utilized to publicize and attract consumption of the content.Consumers with different consumer profiles may be attracted to contentbased upon different factors. For example, some consumption is basedupon preferences toward comedic content, romantic content, actioncontent, and/or particular actors (including their attributes). In oneapproach, a limited selection of depictions of the content is manuallygenerated and distributed in order to attract and maximize consumptionbased upon a large, generalized set of consumer profiles. For example,on movie poster may be manually created for children and one for adults.In another example, one movie poster may be manually created fordistribution in North America, and one for distribution in China.However, such manual creation of images representing content isexpensive and time consuming because each image needs to be createdmanually. Furthermore, some users may not be attracted to any of theelements of generalized content depiction or may even be repelled by allor parts of the depictions. For this reason, such broad targeting isoften ineffective because, for example, not every consumer in NorthAmerica will have the same preferences. Thus, more effective systems andmethods are needed for distributing exemplary depictions (e.g., posters)of content tailored to particular user profiles.

In some embodiments, machine learning (ML)/artificial intelligence (AI)methods and systems are implemented to generate content depictions(e.g., images/posters) based upon user profiles, metadata pertaining tothe content being depicted, content structures and features (e.g.,images extracted from the depicted content and/or other content) andrelated metadata. In an embodiment, a machine learning system isprogrammed to process and interpret user profiles (e.g., contentbrowsing history, prior content consumption, social media patterns, etc.. .) into classifications of features and levels of preference fordifferent kinds of features of content (e.g., particular actors orattributes of actors, scenery, comedic content, romantic content,action-based content, etc.) and utilizing a store of related featuredepictions (e.g., images) from the content being depicted and/or othercontent (e.g., including images of the preferred actor(s), scenery,etc.) and generating a new depiction (e.g., image/poster) that may bedistributed with respect to a particular user profile (e.g., an onlineuser account).

After a depiction is distributed, data may be collected that is relatedto responses to the distribution (e.g., consumption history by useraccounts to which the generated depictions were distributed to). Thisdata may be received by the ML system, with which it may retrain itsprogramming to further optimize output and subsequent outcomes (e.g., toincrease consumption of content). For example, the ML may correlate agreater responsiveness by a particular user (or type of user) profilewith certain features of the generated depictions (e.g., certainbackgrounds, actors, etc.). As the ML system receives more feedback, itcontinues to “learn” and reprogram itself to optimize how to generatedepictions and maximize outcomes (e.g., consumption). It's store (e.g.,images) of features of content may also grow and certain features may beemphasized based upon the “learning.”

In some embodiments, the ML system includes a neural network with whichit learns patterns and determines outputs (depictions). The neuralnetwork may include multiple nodes related to particular features ofcontent and of user profiles. Connections between these nodes and thestrengths of these connections may be programmed based upon historicalmetadata of user profiles as the data pertains to the preference forparticular classified features of content. The neural network may learnto generate new nodes and connections based upon new data it receivesand and/or based upon outcome data collected after content depictionsare generated and distributed.

In some embodiments, a neural network is a generative adversarialnetwork (GAN). The GAN may include a discriminator module that comparesa generated depiction/image with “authentic,” approved, and/orpreviously distributed images/depictions. If the discriminator fails to“pass” the depiction, factors pertaining to the failure may be fed backinto the ML system in order to improve or modify the depiction to moreclosely represent an approved or authentic depiction. For example, the“discriminator” module may determine if the features included in thegenerated depiction flow together naturally (e.g., an actor's depictedproportions are not oversized compared to an object or background scenein the depiction). In addition, the “discriminator” module itself mayalso be reprogrammed and/or modified via feedback loop. In someembodiments, both the ML system and the “discriminator” module may befine tuned in parallel.

A machine learning system may include a natural language processor (NLP)to interpret collected metadata pertaining to a user's account profileand/or content profile. For example, an NLP may interpret posts on asocial media site which reflect that the user profile has a tendency tofavor ocean scenes, car crashes, particular food items, etc. . .Likewise, an NLP may be used to interpret particular features of content(vocabulary) or its metadata with particular situations or themes (e.g.,comedic, romantic, or hostile).

In some embodiments, the ML system utilizes deconstructed segments orfeatures of content in order to learn which features/segments of thecontent are associated with particular themes, characters, scenes, etc.and/or for generating a content depiction tailored to a particular userprofile or collection of user profiles. These segments/features may beclassified as a content structure based on a content segment or otherfeature of content.

A content structure may include objects, where each object includes aset of attributes and corresponding mappings. For example, a movie maybe deconstructed into a plurality of objects each having their ownrespective attributes and mappings. These structures may be assignedparticular attributes that also correlate (e.g., to different levels ofdegree) to attributes of particular user profiles. The ML system maythen identify a correlated structure and use it to generate a depictionor a part of a depiction of content tailored to a particular userprofile. Exemplary content structures that can be used for generatingnew content structures and rendered into a content depiction aredescribed by co-pending application No. 16/363,919 entitled “SYSTEMS ANDMETHODS FOR CREATING CUSTOMIZED CONTENT”, filed on Mar. 25, 2019 (“'919Application”), which is hereby expressly incorporated by referenceherein in its entirety.

Generation of the tailored content structures and/or images helpsovercome the limitations of generalized depictions for large audiencesdescribed above. For example, a user receiving content depictionstailored to their profile or similar profiles according to someembodiments will be apprised of the content features which match theirpreferences and thus is more likely to further consume the content beingdepicted. Generation will also be less time consuming, user intensive,and likely more predictive of positive outcomes than manual generation.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and advantages of the disclosure will beapparent upon consideration of the following detailed description, takenin conjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows an illustrative flowchart of a machine learning system forgenerating tailored content depictions according to some embodiments ofthe disclosure.

FIG. 2 shows an illustrative flowchart of a generative adversarialneural network machine learning system for generating tailored contentdepictions according to some embodiments of the disclosure.

FIG. 3 shows an illustrative diagram of a neural network model nodearray according to some embodiments of the disclosure.

FIG. 4 is a diagram of an illustrative device for generating contentdepictions in accordance with some embodiments of the disclosure;

FIG, 5 shows an illustrative flowchart of a process for generatingcontent depictions in accordance with some embodiments of thedisclosure;

FIG. 6 shows an illustrative flowchart of a neural network process forgenerating content depictions in accordance with some embodiments of thedisclosure;

FIG. 7 shows an illustrative process of combining image data to generatea content depiction in accordance with some embodiments of thedisclosure;

DETAILED DESCRIPTION

In some embodiments of the present disclosure, a machine learning systemutilizes profile input, content input, and a data store of contentstructures (e.g., content images, descriptions, etc.) to generate acontent depiction tailored to the profile input. FIG. 1 shows anillustrative flowchart of a machine learning system for generatingtailored content depictions according to some embodiments of thedisclosure. A machine learning engine 120 receives profile data 125 forwhich a content depiction 145 is generated. Profile data 125 can includecontent preferences, browsing history, and purchase history, such as maybe collected in relation to an online account or profile.

Machine learning engine 120 also receives and/or has access to contentdata, including image data and content structure data relating to aparticular content which is being depicted. Content data can include,for example, meta data identifying the title, actors, script,viewership, and other data pertaining to the depicted content or othercontent. Content structure data can include content structures definedby objects deconstructed from the content itself. The content structuresmay include attribute tables with attributes, such as, for example, theheight, race, age, gender, hair color, eye color, body type, a facialpattern signature, a movement pattern, a relative location with otherobjects, an interaction with other objects, and/or the like. Theattributes may be stored in an attribute table as a listing of datafield names in the content structure. The attributes may also haveassociated mappings. Generation of such content structure may beperformed, e.g., by deconstructing an existing content segment.Deconstruction of content segment and storage of resulting contentstructures is further described, for example, in the ‘919 Applicationreferenced above.

Machine learning system 120 also receives sample depictions 110from/with which to base and compare generated depiction 145. Thesesample depictions 110 may include already generated andauthenticated/approved depictions. Machine learning system 120 utilizesthe input data as well as a database system 115 of image data togenerate a new depiction 145. The image data may include images andtheir attributes (e.g., particular actors, backgrounds, scenes,locations, objects, etc..). The image data may have been previouslyprogrammed into the database system 115 or obtained from content data130 and sample depictions 110.

Machine learning system 120 generates a new content depiction 145 of acontent by combining and modifying elements of image data from contentdata 130 and/or content depictions 110 based upon profile data 125 andcontent data 130. The machine learning system 120 is trained andprogrammed to combine and/or modify image data to reflect determinedcontent preferences associated with profile 125. Machine learning system120 may include one or more machine learning models 123. These modelsmay employ, for example, linear regression, logistic regression,multivariate adaptive regression, locally weighted learning, Bayesian,Gaussian, Bayes, neural network, generative adversarial network (GAN),and/or others known to those of ordinary skill in the art. Multiplemodels may be used with results combined, weighted, and/or otherwisecompared in order to determine an output depiction 145.

Preferences associated with profile 125 may be determined such as bycorrelating profile data (e.g., browsing history, content preferences)with particular attributes of images (e.g., particular actors, actorattributes, themes of action, romance, comedy, etc.). For example, themachine learning system may determine that the profile consumes content(e.g., movies, television programs) with attributes of comedy to agreater degree than content with attributes of action or drama, Themachine learning system 120 can, for example, analyze data (e.g.,credits, reviews, scripts) associated with the consumed content that maybe retrievable from local (e.g., local database systems) or onlinesources (e.g., websites) and include key words (e.g., “comedy,” “funny,”“hilarious”) that the system has been programmed or “learned” to ascribewith particular attributes (e.g., themes of comedy). In someembodiments, the machine learning system utilizes a natural languageprocessor (NLP) to analyze the data and extract attributes of thecontent.

The machine learning system 120 may use one or more of contentdepictions 110 as a reference depiction. These may include presentlyapproved/active depictions associated with the content and theattributes associated with the depictions (e.g., actors, scenedescription, background, location, etc. . . .). The machine learningsystem 120 may then tailor a reference depiction 110 or generate asubstantially new depiction based upon the determined preferencesassociated with the profile 25. For example, the machine learning system20 may determine that most of the attributes of a content depiction 110correspond to preferences of the profile 125 and thus may eitherminimally or decline to modify a selected content depiction. Forexample, the machine learning system 120 may simply substitute thebackground image of a depiction with a background image from the imagedatabase 115 with attributes (e.g., outdoor daytime scene) that moreclosely correspond to the preferences of profile 125.

In some embodiments, the machine learning system 120 may generate asubstantially new depiction (e.g., an image or content structure thatrepresents an image) utilizing image data/content structure data fromimage database system 115. For example, when a particular profilepredilects to romance themes, and the selected depictions 110 includerelatively little if any attributes of romance, the machine learningsystem 120 may pull images/content structure from image/contentstructure database 115 of two actors associated with the content andsuperimpose their images/content structure in an embrace over abackground image/content structure with romantic attributes (e.g., asfurther shown in FIG. 8).

Once a depiction 145 has been generated, it may be transmitted at block150 to a destination associated with profile 125 (e.g., for display in awebpage downloaded using a browser using a “cookie” linked to theprofile). The destination may include devices for personal displays ofcontent (e.g., streaming media, live television) linked to profile 125.For example, a user associated with profile 125 may login to a streamingor live content account or service (e.g., Tivo Edge™for Cable, AmazonPrime Video, Xfinity Cable, etc. . . .). During a broadcast of contentusing the associated device and/or service, an interval between orduring periods of content delivery may include display of the generateddepiction and may include providing information or an interface foraccessing (e.g., viewing/recording) content (e.g., streaming/livebroadcast content) associated with the depiction.

After transmission of content depiction 145 at block 150, feedback data(e.g., metadata) may be collected at block 155 in connection with itstransmission. Data reflecting consumption of the content (e.g.,consumption in response to or proximate to the display of the contentdepiction) may be collected and transmitted back to the machine learningengine 120. For example, a Tivo Edge™device may be programmed to storerecords of consumption of the content before and immediately afterdisplay of the generated content depiction and also consumption of thecontent in response to other content depictions and/or consumption ofcontent absent a proximate display of any content depiction.

After receiving the feedback data collected at block 155, machinelearning system 120 may use the feedback data to further program itselffor purposes of generating further content depictions. For example,machine learning system may correlate certain content depictions oraspects thereof with greater consumption of the content by specificprofiles or profiles with particular characteristics (e.g.,predilections for romance, action, etc.).

FIG. 2 shows an illustrative flowchart of a generative adversarialneural network (GAN) machine learning system for generating tailoredcontent depictions according to some embodiments of the disclosure. Acontent depiction generator 230 network module receives data 215 for aparticular content, profile data 200, and collected metadata 210pertaining to the particular content and other content. Data 215 mayinclude data describing the content including, for example, its actors,themes, story summary, etc. Data 215 may also include content structuressuch as described herein including content objects that may be used togenerate the content or variations thereof. Data 215 may include contentdepictions, content structures, and images from which new depictions maybe based as described herein.

Collected metadata 210 may include, for example, content consumptionstatistics for the content to be depicted and/or other related content.For example, the metadata 210 may include data pertaining to the actorsof the content, their relative popularity, the success of particularcontent they have been involved in, the success of particular contentdepictions related to the content, and other data that may be used totailor a content depiction using generator module 230.

Profile data 200 may include content preferences and consumption historyassociated with a particular profile. Profile data 200 may includeinternet browsing history, social media posts, content “likes” or“dislikes,” and other data that may be analyzed by generator 230 todetermine content preferences associated with a profile such as furtherdescribed herein.

Generator module 230 may also be programmed to generate tailored contentutilizing a store 235 of image data and model content depictions 250.Such as described with respect to FIG. 1, image data may include imagesof particular actors, backgrounds, scenes, objects, etc., and theirattributes. Content depictions 250 may include previously generated andapproved (model) content depictions.

Generator module 230 includes a neural network of nodes and nodeconnections programmed to determine and generate a tailored contentdepiction based upon content data 215, profile data 200 and metadata210. An exemplary network of nodes and connections is shown anddescribed with respect to FIG. 3. The nodes and connections, store 235of images, and model depictions 250 may be pre-programmed to a certainlevel as a basis for generating initial content depictions. As will bedescribed further, generator module 230 is programmed to generate newnodes and connections for content depiction generation based uponfeedback and fine tuning from block 265 and a discriminator module 240.Discriminator module 240 may include a neural network which isprogrammed with nodes and connections to discriminate between passabledepictions and those that fail discrimination.

Generator module 230 may pre-process profile data 200 and metadata 210to determine particular preferences associated with a profile. Forexample, generator module 230 can compare content consumption historyprovided in profile data 200 to metadata 210 or content data 215relating to the content consumed (e.g., keywords, actors, descriptionsof the content) to determine a profile preference for particular contentattributes. Profile data 200 may also include predetermined profilepreferences.

Using determined profile preferences and content data as an input, aneural network of generator 230 operates to modify an existing contentdepiction or generate a new depiction from various image data elementsfrom image data store 235. For example, an input reflecting a highdegree of preference for a particular content attribute (e.g., aparticular actor or content theme) may cause the neural network to applya node and strong connection for incorporating an image/contentstructure attribute with that particular attribute (e.g., animage/content structure of a particular actor or content backdrop). Theneural network may utilize numerous such nodes and connections balancedagainst each other to modify or create a depiction with variousattributes.

After a depiction is generated by generator 230, discriminator module240 compares the generated depiction to one or more model contentdepictions 250 at 255. The discriminator 240 may apply analysis andcomparisons, including the use of a neural network, to determine if thegenerated depiction satisfies particular criteria pertaining toauthentic/approved content depictions. Analysis/comparisons may include,for example, determining whether features (e.g., images/contentstructures of actors, objects, backgrounds) sufficiently resemblefeatures of the model depictions. Various image/content structureprocessing functions (e.g., facial/object recognition, pattern matching)may be employed to perform the analysis/comparisons. Based upon theanalysis/comparisons, a determination is made about whether thegenerated depiction satisfies the criteria/comparisons to a sufficientdegree at block 245.

If, at block 245, the generated depiction doesn't satisfy the testsperformed by discriminator 240 and/or other examinations/criteria (e.g.,approval/rejection through an external process/operator), feedback dataregarding the rejection may be received by the generator 230 and thediscriminator 240. Feedback data may include, for example, rejections ofparticular identified actors, scenes, backgrounds, and/or objects withinthe content depiction. Feedback data may include data indicatingattributes that should be introduced, removed, and/or modified in thedepiction. Based upon the feedback, generator module 230 maygenerate/modify a content depiction and again output the newly generateddepiction for further processing by discriminator module 240. The cyclemay continue until a satisfactory depiction is generated and/or aparticular threshold of rejections is exceeded.

At block 260, the generated depiction is distributed such as across acomputer network and to a content platform. In some embodiments, thedepiction is distributed in a manner that is linked with a particularaccount profile (e.g., a content distribution platform linked to theprofile) or type of profile. As described herein, the feedback datapertaining to the distribution of the depiction and related contentconsumption may be collected and received at block 265 and used toupdate medadata store 210 and/or profile data 200. The feedback data maybe fed back into generator module 230 or discriminator module 240 andresult in reprogramming of the generator 230/discriminator 240 such asbased upon analysis of the generated depiction(s), related contentconsumption, and profile data.

FIG. 3 shows an illustrative diagram of a neural network model nodearray 300 according to some embodiments of the disclosure. An inputlayer 310 may include various input nodes 350 matched to particularprofile attributes (e.g., particular types of content preferences). Theinput nodes may also include inputs to various image data, contentstructures, and content data. These input nodes may be connected,designated by varying degrees of connection strength, to other nodeswithin a processing layer 320 of nodes. The processing layer 320 ofnodes directs the neural network to modify or generate a contentdepiction based upon connections to the input layer and to other nodeswithin the processing layer 320. The processing layer processes theinput depending on the current state of the network's adaptiveprogramming. The processing layer may have direct access to animage/content structure data store (e.g., data/content structure store235 of FIG. 2) from which image data is used to generate and/or modifycontent depictions. Model node array 300 may be used within a neuralnetwork generator module such as generator module 230 of FIG. 2.

Based upon the processing in the processing layer 320, an outputdepiction is generated through the output layer 330. The output layer330 produces an output content depiction with various attributesdetermined through the input and processing layers 310 and 320. Theoutput depiction may be further forwarded to a discriminator module(e.g., module 240 of FIG. 2) and/or distributed such as furtherdescribed herein. After a depiction is forwarded to a discriminatorand/or distributed, the neural network may be (re-)programmed based uponfeedback received in response. For example, feedback data may indicate agreater relative positive response (e.g., consumption of content) fromparticular profile types to particular image/content structureattributes. The neural network may thus be reprogrammed to strengthen aconnection (association) between a particular profile and image/contentstructure attribute.

FIG. 4 is a diagram of an illustrative device 400 used for generating,distributing, and displaying content depictions in accordance with someembodiments of the disclosure. A system for generating and distributingcontent depictions may include, for example, servers, data storagedevices, communication devices, display devices, and/or other computerdevices. Control circuitry 404 may be based on any suitable processingcircuitry such as processing circuitry 406. As referred to herein,processing circuitry should be understood to mean circuitry based on oneor more microprocessors, microcontrollers, digital signal processors,programmable logic devices, field-programmable gate arrays (FPGAs),application-specific integrated circuits (ASICs), etc., and may includea multi-core processor (e.g., dual-core, quad-core, hexa-core, or anysuitable number of cores) or supercomputer.

In some embodiments, processing circuitry 406 may be distributed acrossmultiple separate processors or processing units, for example, multipleof the same type of processing units (e.g., two Intel Core i7processors) or multiple different processors (e.g., an Intel Core i5processor and an Intel Core i7 processor). A network interface 410 maybe used to communicate with other devices in a machine learning system(e.g., an image database system 15 of FIG. 1) or with devices to whichcontent depictions are distributed (e.g., content servers or contentdisplay devices).

In some embodiments, control circuitry 404 executes instructions forexecution of a machine learning system stored in memory (i.e., storage408). The instructions may be stored in either a non-volatile memory 414and/or a volatile memory 412 and loaded into processing circuitry 406 atthe time of execution. A system for generating content depictions (e.g.,the systems described in reference to FIGS. 1, 2, and 3) may be astand-alone application implemented on a media device and/or a server ordistributed across multiple devices in accordance with device 400. Thesystem may be implemented as software or a set of executableinstructions. The instructions for performing any of the embodimentsdiscussed herein of content depiction generation may be encoded onnon-transitory computer-readable media (e.g., a hard drive,random-access memory on a DRAM integrated circuit, read-only memory on aBLU-RAY disk, etc.) or transitory computer-readable media (e.g.,propagating signals carrying data and/or instructions). For example,instructions in accordance with the processes of FIGS. 5, 6, and 7 maybe stored in storage 408, and executed by control circuitry 404 ofdevice 400.

FIG. 5 shows an illustrative flowchart of a process for generatingcontent depictions in accordance with some embodiments of thedisclosure. At block 510, profile data is received at a machine learningsystem (e.g., machine learning system FIGS. 1 and 2). As describedherein, profile data can include content preferences, browsing history,content consumption history, and social media history. At block 520,profile preferences are identified such as based upon analyzing theprofile data. A set of resulting profile preference inputs is thenfurther processed by the machine learning system for generating anoutput depiction.

At block 530, the machine learning system receives and/or accessescontent structures and/or image data associated with a content to bedepicted or related to other content. At block 540, the machine learningsystem may classify the received/accessed content structures and/orimage data according to content categories. In some embodiments,accessed content structures and/or image data may already be classifiedwithin the machine learning system. For example, images and/or contentstructures of particular actors, objects, background scenes, etc., maybe accessible within an image database and/or a content structure store(e.g., image/content structure database store 15 of FIG. 1).

At block 550, the machine learning system may use one or more trainedmodels for correlating profile preferences with content structures,images, or image features. These models may employ, for example, linearregression, logistic regression, multivariate adaptive regression,locally weighted learning, Bayesian, Gaussian, Bayes, neural network,generative adversarial network (GAN), and/or others known to those ofordinary skill in the art. Multiple models may be used with resultscombined, weighted, and/or otherwise compared.

At block 560, the model(s) are utilized to generate a contentstructure/image depiction of identified content based upon the profilepreferences and correlated content structures, images, and/orimage/content structure features as further described herein. Theresulting content structure/image depiction may be further analyzedand/or modified, and/or the model(s) reprogrammed, such as describedwith respect to the GAN of FIG. 2. The generated depiction may be in theform of an image and/or a content structure represented by one or moreobjects (e.g., images, image attributes, vector graphic commands, etc.)that can be employed or converted for example, to generate an imagedepiction. After generation, the depiction may be distributed such as toa target audience (e.g., an account associated with the profile) and maybe presented in the context of a promotion or link to consumption ofcontent associated with the content depiction (e.g., by way of a webpage or content guidance/selection/viewing system). An image depictionor an image based upon the generated content structure depiction may becreated for display on a screen to a target audience such as using thetechniques described in the ‘919 Application. Image conversion from thecontent structure/depiction may occur in whole or part using devicesincluding those which are used to generate the contentstructure/depiction and device(s) from which the image is displayed.

At block 570, in response to distribution of the content depiction atblock 560, feedback data may be collected. The feedback data may includeconsumption of content, ratings, and/or social media posts pertaining tothe content depiction structure and image depictions generatedtherefrom. At block 580, the model(s) of the machine learning system maybe reprogrammed based upon the feedback such as to improve correlationand generation of content depictions that induce increased contentconsumption as further described herein. After reprogramming, themachine learning system may receive further profile data at block 510for generating a new depiction based upon the reprogramming.

FIG. 6 shows an illustrative flowchart of a neural network process forgenerating content depictions in accordance with some embodiments of thedisclosure. At block 610, profile data reflecting preferences of aprofile (e.g., user account profile) is received at a neural networksystem such as described, for example, with respect to FIGS. 2 and 3. Atblock 620, content preferences for the profile are identified (e.g.,based upon content consumption history, browsing history, social mediaposts, etc.,) such as further described herein.

At block 630, a neural network further accesses/receives contentstructures and/or image data that are or can be classified withparticular attributes (e.g., particular actors, backgrounds, themes,etc.). For example, the neural network may utilize a deconstructionengine as described above to break down content into content structuresand objects having particular attributes (e.g., as described in the ‘919Application). At block 640, the nodes and connections of the neuralnetwork may be programmed (or reprogrammed) according to classifiedcontent structures and/or image data received or accessed at block 630,using profile data, and/or feedback data received in response to contentdepiction distribution (e.g., described below in reference to block690).

At block 650, the neural network nodes and connections process theprofile preferences and utilize available content structures and/orimage data to generate a content depiction at block 660. The contentdepiction may be an image depiction and/or a content structure which maybe used to generate an image depiction for optimal induction of contentconsumption based upon the profile preferences. At block 670, thecontent depiction is processed by a discriminator (e.g., discriminatormodule 240 of FIG. 2). Such as described herein, a discriminator maycompare the depicted content to one or more model depictions ordepiction properties/attributes. If the comparison fails particularcriteria (e.g., such as learned by the discriminator to determinepassable/acceptable depictions), the neural network may reprogram itselfbased upon the failing attributes and regenerate another depiction atblock 540 in order to address the failed criteria. The neural networkmay reprogram itself also based upon passing depictions and “learn” tomore efficiently generate passing content depictions.

At block 680, if the content depiction passes discrimination at block670, the content depiction is distributed such as across a computernetwork for display in a device associated with the user profile. Atblock 690, feedback data collected in response to the generated anddistributed content depiction is received by the neural network systemand used to reprogram the nodes and connections of the network at block640. For example, connections in the neural network may be modified orreinforced based upon a negative or positive degree of consumption ofcontent in relation to the content depiction.

FIG. 7 shows an illustrative process of combining image/contentstructure data to generate a content depiction in accordance with someembodiments of the disclosure. In an embodiment, a machinelearning/artificial intelligence system generates a content depiction730 of a content. Data associated with a particular profile is used bythe machine learning/artificial intelligence system to tailor thedepiction to reflect preferences of the profile. As described in variousembodiments herein, a machine learning/artificial intelligence systemaccesses and/or receives content structures and image data includingimage data 710A, 710B, and 710C and associated object structures 715A,715B, and 715C, respectively, for generating a content depiction 730.

Image data/content structures may include image data objects thatrepresent particular characters/actors such as images 710B and 710C,respectively. Image data objects/content structures may include or bedefined by associated object data structures 715B and 715C includingcharacter attributes or other attributes associated with the imagesincluding character roles in a content, gender, relative scales of theimages, etc. Object data structures are further described, for example,within the ‘919 application referenced above.

The input profile data may reflect a preference for one or more of theseobject attributes, based upon which the system may be directed togenerate a depiction including these characters. Additional profile datamay reflect a preference for romantic themes, for example, which mayfurther direct the machine learning/artificial intelligence system togenerate a depiction with the characters in an embrace and a backgroundrepresenting a romantic theme (e.g., a moonlit night) such asexemplified in image data 710A and associated object structure 715A. Anexemplary depiction 730 combining these various attributes may thenresult from the system to reflect preferences of the profile.

The above-described embodiments of the present disclosure are presentedfor purposes of illustration and not of limitation, and the presentdisclosure is limited only by the claims which follow. Furthermore, itshould be noted that the features and limitations described in any oneembodiment may be applied to any other embodiment herein, and flowchartsor examples relating to one embodiment may be combined with any otherembodiment in a suitable manner, done in different orders, or done inparallel. In addition, the systems and methods described herein may beperformed in real time. It should also be noted, the systems and/ormethods described above may be applied to, or used in accordance with,other systems and/or methods.

1.-22. (Canceled)
 23. A method comprising: (a) receiving profile dataassociated with a user; (b) receiving metadata of a video item; (c)retrieving, from a database of movie posters, a plurality of verifiedmovie posters; (d) accessing a neural network framework, wherein theneural network framework comprises a generator neural network and adiscriminator neural network; (e) generating a proposed movie poster, byinputting into the generator neural network of the neural networkframework: (a) the profile data, (b) the metadata of the content item,and (c) the plurality of verified movie posters; (f) determining whetherthe proposed movie poster is accepted by inputting into thediscriminator neural network of the neural network framework: (a) theproposed movie poster generated by the generator neural network, and (b)the plurality of movie posters; (g) in response to determining that theproposed movie poster is not accepted: modifying configurations of thegenerator neural network of the neural network framework and thediscriminator neural network of the neural network framework; andrepeating steps (e)-(f); (h) in response to determining the proposedmovie poster is accepted: storing the proposed movie poster in a contentplatform association with the video item; and generating for display thestored proposed movie poster on the content platform.
 24. The method ofclaim 23, further comprising: causing distribution of the proposed movieposter across a computer network to at least one network deviceassociated with the profile data.
 25. The method of claim 24, whereinthe profile data comprises at least one of viewing of streaming content,internet browsing history, or social media activity.
 26. The method ofclaim 23, wherein profile data comprises preferences of genre comprisingat least one of action, violence, romance, comedy, mystery, sciencefiction, or drama.
 27. The method of claim 23, wherein profile datacomprises preferences for at least one of actors, actor attributes,emotions, background scenery, geographic location, colors, or animals.28. The method of claim 23, wherein the generator neural network has aninput layer having nodes representing (a) the profile data, (b) themetadata of the content item, and (c) the plurality of verified movieposters, and a processing layer of nodes and connections between them,the nodes and connections programmed and configured to output theproposed movie poster to an output layer.
 29. The method of claim 28,wherein the discriminator neural network is programmed to compare theproposed movie poster with features of at least one of the plurality ofverified movie posters.
 30. The method of claim 29, wherein thediscriminator neural network comprises an input layer of nodesrepresenting the proposed movie poster and the plurality of verifiedmovie posters, and a processing layer of nodes and connections betweenthem, the nodes and connections programmed and configured to output adetermination of whether the proposed movie poster satisfies criteria ofan acceptable movie poster.
 31. The method of claim 30, wherein thegenerator neural network and discriminator neural network are trained byfeedback data, and wherein the generator neural network is trained bythe discriminator neural network determination of whether the proposedmovie poster satisfies criteria of an acceptable movie poster.
 32. Themethod of claim 31, wherein feedback data comprises content consumptiontracked in response to distribution of the proposed movie poster.
 33. Amachine learning system comprising one or more processors programmedwith instructions to cause the one or more processors to perform: (a)receive profile data associated with a user; (b) receive metadata of avideo item; (c) retrieve, from a database of movie posters, a pluralityof verified movie posters; (d) access a neural network framework,wherein the neural network framework comprises a generator neuralnetwork and a discriminator neural network; (e) generate a proposedmovie poster, by inputting into the generator neural network of theneural network framework: (a) the profile data, (b) the metadata of thecontent item, and (c) the plurality of verified movie posters; (f)determine whether the proposed movie poster is accepted by inputtinginto the discriminator neural network of the neural network framework:(a) the proposed movie poster generated by the generator neural network,and (b) the plurality of movie posters; (g) in response to determiningthat the proposed movie poster is not accepted: modify configurations ofthe generator neural network of the neural network framework and thediscriminator neural network of the neural network framework; and repeatsteps (e)-(f); (h) in response to determining the proposed movie posteris accepted: store the proposed movie poster in a content platformassociation with the video item; generate for display the storedproposed movie poster on the content platform.
 34. The machine learningsystem of claim 33, further programmed with instructions to cause theone or more processors to perform: cause distribution of the proposedmovie poster across a computer network to at least one network deviceassociated with the profile data.
 35. The machine learning system ofclaim 34, wherein the profile data comprises at least one of viewing ofstreaming content, internet browsing history, or social media activity.36. The machine learning system of claim 33, wherein profile datacomprises preferences of genre comprising at least one of action,violence, romance, comedy, mystery, science fiction, or drama.
 37. Themachine learning system of claim 33, wherein profile data comprisespreferences for at least one of actors, actor attributes, emotions,background scenery, geographic location, colors, or animals.
 38. Themachine learning system of claim 33, wherein the generator neuralnetwork has an input layer having nodes representing (a) the profiledata, (b) the metadata of the content item, and (c) the plurality ofverified movie posters, and a processing layer of nodes and connectionsbetween them, the nodes and connections programmed and configured tooutput the proposed movie poster to an output layer.
 39. The machinelearning system of claim 38, wherein the discriminator neural network isprogrammed to compare the proposed movie poster with features of atleast one of the plurality of verified movie posters.
 40. The machinelearning system of claim 39, wherein the discriminator neural networkcomprises an input layer of nodes representing the proposed movie posterand the plurality of verified movie posters, and a processing layer ofnodes and connections between them, the nodes and connections programmedand configured to output a determination of whether the proposed movieposter satisfies criteria of an acceptable movie poster.
 41. The machinelearning system of claim 40, wherein the generator neural network anddiscriminator neural network are trained by feedback data, and whereinthe generator neural network is trained by the discriminator neuralnetwork determination of whether the proposed movie poster satisfiescriteria of an acceptable movie poster.
 42. The machine learning systemof claim 41, wherein feedback data comprises content consumption trackedin response to distribution of the proposed movie poster.